A market supervision dynamic monitoring and intelligent risk early warning system and method based on big data

Abstract

The application discloses a market supervision dynamic monitoring and intelligent risk early warning system and method based on big data, and relates to the technical field of monitoring and early warning.The application performs full-process dynamic monitoring on market supervision tasks through a dynamic monitoring module, sets a risk analysis module to intelligently analyze abnormal behaviors or trends of market subjects, extracts features and classifies risks of monitoring data through a chaotic immune optimization algorithm and a migration immune algorithm, and generates a risk early warning report including risk levels, risk types and corresponding disposal suggestions; based on analysis results of the risk analysis module, the application displays the stock, increment, industry trend and regional distribution of market subjects in a visual manner, integrates an early warning notification function, sends risk early warning results to relevant supervision departments, supports priority processing of different levels of risks and a multi-department collaborative early warning mechanism, and realizes full-life cycle dynamic supervision and intelligent risk early warning of market subjects.

Description

Technical Field

[0001] The present invention relates to the field of monitoring and early warning technology, and in particular to a market supervision dynamic monitoring and intelligent risk early warning system and method based on big data. Background Art

[0002] Currently, with rapid socioeconomic development and the continuous expansion of the market, market regulation is becoming increasingly complex and challenging. The supervision of market entities involves a variety of data sources, including registration information, credit records, consumer complaints, and food and drug regulations. This data is scattered across various departments and platforms, lacking a unified integration mechanism, leading to serious data silos. Furthermore, most current market supervision systems lack the ability to dynamically monitor market entities, particularly in order to grasp real-time changes in their risk factors, potentially missing early warning windows for potential issues.

[0003] Traditional risk analysis methods are mainly based on static indicators, which cannot adapt to the dynamic changes in the risk characteristics of market entities. They have weak capabilities for collaborative risk assessment of cross-regional and cross-industry market entities and are prone to regulatory blind spots.

[0004] Therefore, there is a need for a market supervision dynamic monitoring and intelligent risk early warning system and method based on big data to achieve efficient integration of data resources, intelligent risk identification and prediction, and dynamic market supervision. Summary of the Invention

[0005] In response to the above problems, the present invention proposes a market supervision dynamic monitoring and intelligent risk early warning system and method based on big data.

[0006] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0007] A market supervision dynamic monitoring and intelligent risk early warning system based on big data, the system comprising:

[0008] The data collection module is used to collect market supervision data from multiple heterogeneous data sources, including market entity registration information, business behavior data, credit records, law enforcement inspection data, consumer complaint data, food and drug supervision data, and external data related to market supervision;

[0009] A data processing module is used to clean, convert and integrate the market supervision data, eliminate data redundancy and inconsistency, and generate unified structured data. The structured data includes basic attribute information of market entities, dynamic monitoring indicator data, and input data required for risk analysis;

[0010] A dynamic monitoring module, used to dynamically monitor the entire process of market supervision tasks based on the structured data;

[0011] The risk analysis module is used to conduct intelligent analysis of abnormal behaviors or trends of market entities based on the real-time monitoring results of the dynamic monitoring module. It uses the chaos immune optimization algorithm and the migration immune algorithm to extract features and classify risks from the monitoring data, and generates a risk warning report that includes risk level, risk type, and corresponding disposal suggestions;

[0012] The visualization display module is used to display the stock, growth, industry trends and regional distribution of market entities in a visual way based on the analysis results of the risk analysis module. The visualization methods include line charts, bar charts, geographic information system maps and report forms, which dynamically display risk warning information; and integrate the warning notification function, and send the risk warning results to relevant regulatory departments via SMS, email or system push, supporting priority processing of risks at different levels and multi-department collaborative warning mechanism.

[0013] As a preferred solution of the present invention, the data collection module dynamically adjusts the collection priority based on the activity frequency and importance of the market entity. The activity frequency is calculated based on the real-time business behavior data of the market entity, and the importance is calculated based on the industry risk weight, credit score fluctuation and historical abnormal behavior frequency of the market entity.

[0014] By defining the trust level and contribution level of data sources, the collection ratio of data sources is dynamically optimized. The formula is:

[0015]

[0016] Where, P v represents the priority acquisition probability of data source v; T v is the trustworthiness of data source v; C v is the contribution of data source v; V is the total number of data source types.

[0017] As a preferred solution of the present invention, the data processing module adjusts the sensitivity of the data cleaning rule by generating a dynamic threshold by introducing a chaotic mapping function. The calculation formula of the dynamic threshold is:

[0018] θ(t)=μ·θ(t-1)·(1-θ(t-1))+γ·f error (t);

[0019] Where θ(t) is the dynamic threshold of the current iteration; θ(t-1) is the dynamic threshold of the previous iteration; μ is the chaos parameter; γ is the disturbance coefficient; f error (t) is the error function of the current iteration;

[0020] By introducing the semantic weight w s and contextual relevance r cDynamically adjust the integration priority of data fields. The formula is:

[0021]

[0022] Where S merge Score the match between the two tables: (F b ,G b ) is the set of fields to be integrated; B is the total number of fields; Sim(F b ,G b ) indicates field F b and G b The semantic similarity of w s is the semantic weight; r c It is context-dependent.

[0023] As a preferred solution of the present invention, the full-process dynamic monitoring includes:

[0024] Monitoring of task execution: obtaining task name, start time, end time, execution status, duration, and execution log information;

[0025] Monitoring of data volume changes: Based on the data table change rate, identify data tables with significant data volume differences, calculate the data table change rate and compare it with the preset threshold to determine if the change rate|V new -V old | / V old If the value of V exceeds the preset threshold, it is considered to be significantly different and a data table warning message is generated. The data table warning message includes the name of the data table, the rate of change, the abnormality type and the warning generation time; wherein, V new Indicates the amount of data in the current data table, V old Indicates the amount of data in the data table during the previous detection;

[0026] Database resource monitoring: monitors database disk usage, including used space, remaining space, total space, and utilization, and generates resource utilization warning information;

[0027] Dynamic monitoring of core indicators: Real-time acquisition of changes in core indicators of market entities, including the number of existing households, incremental trends, registered capital change rate, industry distribution trends and regional distribution trends.

[0028] As a preferred solution of the present invention, the risk analysis module includes:

[0029] The data preprocessing unit is used to obtain market entity monitoring data from the dynamic monitoring module and perform data cleaning and normalization;

[0030] Chaos immune optimization unit, used to extract features from pre-processed data using a chaos immune optimization algorithm, optimize the antibody population, and obtain a preliminary risk fitness value;

[0031] Migration Immunity Optimization Unit, used to optimize migration and dynamically adapt risk factors across regions;

[0032] The risk level assessment unit is used to generate the risk level of the market entity based on the optimized risk data;

[0033] The risk type analysis unit is used to further analyze the risk types of market entities based on risk levels, including credit risk, abnormal behavior risk, and abnormal growth risk;

[0034] The risk warning report generation unit is used to generate a visual risk warning report based on the risk level and risk type.

[0035] As a preferred solution of the present invention, a dynamic weight function ω(t) is introduced into the chaotic immune optimization algorithm unit, and an improved chaotic mapping formula is used to generate the antibody population. The formula is:

[0036] A(t+1)=μ·w(t)·A(t)·(1-A(t))+γ·f(A(t));

[0037] Where t is the current iteration number; A(t) is the current risk characteristic value of the market entity; A(t+1) is the risk characteristic value of the market entity after chaos optimization and perturbation at the next moment; μ is the chaos parameter; γ is the perturbation coefficient; f(A(t)) is the fitness function;

[0038] Calculate the fitness of each antibody: where R n is the risk factor value, is the benchmark value, n is the index of the current risk factor; N is the total number of risk factors;

[0039] In the process of population evolution, combined with the risk factor matrix R of the market entity f Adjust the mutation probability, the formula is:

[0040]

[0041] Where, P m is the mutation probability of the current market entity; P m0 is the initial mutation probability; ||R f || is the Euclidean norm of the market entity risk factor;

[0042] The optimized antibody population A(t+1) is generated as the input of the migration immunity optimization unit.

[0043] As a preferred solution of the present invention, in the migration immunity optimization unit, market entities are divided according to industry or region to form distributed antibody nodes, and the migration probability is adjusted based on similarity and time decay:

[0044]

[0045] Where, P trans (i, j, t) is the probability of antibody migration of market entities from region i to region j; Sim(A i ,A j ) is the similarity between the antibodies of the market players in region i and region j; ∑ k≠i Sim(A i ,A k ) is the sum of similarities between region i and all other regions; λ is the time decay coefficient;

[0046] Recalculate the environmental fitness F for the migrated antibodies env :F env =a·R growth +β·R deviation , where R growth represents the growth rate, R deviation represents the risk deviation; α and β are the weight coefficients of growth rate and risk deviation respectively;

[0047] The optimized antibody population and fitness data are transmitted to the risk level assessment unit.

[0048] As a preferred solution of the present invention, in the risk level assessment unit, the formula for generating the risk level of the market entity is:

[0049] f multi (x) = x1·f risk (x)+x2·f priority (x);

[0050] Where, f multi (x) is the final optimized value; f risk (x) is the risk deviation; f priority (x) is the priority of the regulatory task; x1 and x2 are the weight coefficients of risk assessment and regulatory task priority respectively; x represents the characteristic variable of the current market entity;

[0051] Risk levels are divided according to the final optimization value:

[0052] When the final optimized value f multi (x) When a market entity exceeds a set high-risk threshold, it is classified as a high-risk entity and requires priority supervision or emergency intervention measures;

[0053] When the final optimized value fmulti (x) When a market entity is between the high-risk threshold and the low-risk threshold, it is classified as a medium-risk entity and requires regular monitoring and appropriate supervision;

[0054] When the final optimized value f multi (x) When the risk is less than the set low-risk threshold, the market entity is classified as a low-risk entity and only requires routine supervision.

[0055] A market supervision dynamic monitoring and intelligent risk early warning system based on big data early warning method, the method comprising:

[0056] Collect market regulatory data from multiple heterogeneous data sources, cleanse, convert and integrate them, eliminate data redundancy and inconsistency, and generate unified structured data;

[0057] Based on the structured data, the system dynamically monitors the entire process of market supervision tasks, and intelligently analyzes abnormal behaviors or trends of market entities based on the real-time monitoring results of the dynamic monitoring module. It extracts features and classifies risks from the monitoring data using the chaotic immune optimization algorithm and the migration immune algorithm, and generates a risk warning report that includes risk level, risk type, and corresponding disposal recommendations.

[0058] Based on the analysis results of the risk analysis module, the stock, growth, industry trends and regional distribution of market entities are displayed in a visual way, and risk warning information is displayed dynamically; and the warning notification function is integrated to send risk warning results to relevant regulatory departments via SMS, email or system push, supporting priority processing of risks at different levels and multi-department collaborative warning mechanism.

[0059] The beneficial effects of the present invention are: it realizes the efficient integration and structured processing of multi-source data, eliminates data redundancy and inconsistency, and provides a high-quality data basis for dynamic monitoring and risk analysis; it improves the real-time monitoring capability of market supervision tasks, and can dynamically capture the stock, increment, industry distribution trend and regional distribution of market entities, and realizes comprehensive coverage of full-process supervision; based on the chaos immune optimization algorithm and the migration immune algorithm, it accurately extracts the risk characteristics of market entities, intelligently generates risk levels and early warning reports, and provides scientific risk assessment support for regulatory authorities; it intuitively displays the dynamic changes and risk distribution of market entities through multi-dimensional visualization, integrates priority processing and multi-department collaborative early warning mechanisms, and significantly improves supervision efficiency and disposal effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0061] in:

[0062] Figure 1 It is a modular structure diagram of the system of the present invention;

[0063] Figure 2 Flowchart of a method in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of the present invention.

[0065] like Figure 1 FIG. 1 is an embodiment of the present invention, which provides a market supervision dynamic monitoring and intelligent risk early warning system based on big data, including:

[0066] (1) Data acquisition module

[0067] Used to collect market supervision data from multiple heterogeneous data sources, including market entity registration information, business behavior data, credit records, law enforcement inspection data, consumer complaint data, food and drug supervision data, and external data related to market supervision.

[0068] The data acquisition module further includes:

[0069] Dynamic data priority collection mechanism: Dynamically adjust the collection priority based on the activity frequency and importance of market entities. Activity frequency is calculated based on the real-time business behavior data of market entities, and importance is calculated based on the industry risk weight, credit score fluctuations and historical abnormal behavior frequency of market entities;

[0070] Quality-aware optimization collection mechanism based on multi-source data fusion: By defining the trust level and data contribution of data sources, the collection ratio of data sources is dynamically optimized. The formula is:

[0071]

[0072] Where, P vrepresents the priority acquisition probability of data source v; T v is the trustworthiness of the data source v, calculated based on the historical collection accuracy and delay rate of the data source; C v is the contribution of data source v, which is dynamically calculated based on the importance of the collected data to the market entity monitoring indicators; V is the total number of data source types, that is, the number of different data source types involved in the collection decision, such as market entity registration information, credit records, consumer complaint data, etc.

[0073] The dynamic data collection priority mechanism integrates the frequency and importance of market participants' activities, breaking through the fixed constraints of traditional collection order. The quality-aware optimized collection mechanism dynamically allocates collection proportions based on the trust and contribution of data sources, avoiding the uniform processing of conventional collection. Through these mechanisms, the data collection module can prioritize the collection of high-quality and high-priority data even when the data volume is large and the quality is uneven, improving the system's real-time performance and monitoring accuracy.

[0074] (2) Data processing module

[0075] It is used to clean, convert and integrate market supervision data, eliminate data redundancy and inconsistency, and generate unified structured data. The structured data includes basic attribute information of market entities, dynamic monitoring indicator data and input data required for risk analysis.

[0076] The data processing module further includes:

[0077] Data cleaning method based on chaos optimization: The sensitivity of data cleaning rules is adjusted by introducing a chaotic mapping function to generate a dynamic threshold. The calculation formula of the dynamic threshold is:

[0078] θ(t)=μ·θ(t-1)·(1-θ(t-1))+γ·f error (t);

[0079] Where θ(t) is the dynamic threshold of the current iteration, which is used to adjust the sensitivity of the data cleaning rule; θ(t-1) is the dynamic threshold of the previous iteration, which is used to recursively calculate the current dynamic threshold; μ is the chaos parameter, which is used to control the dynamic range of the threshold change, and is usually set in the range of (0, 4); γ is the disturbance coefficient, which is used to dynamically adjust the rule update amplitude; f error (t) is the error function of the current iteration, which is calculated based on the redundancy rate, inconsistency rate or outlier ratio of the data and is used to dynamically correct the threshold;

[0080] Format conversion and integration algorithm based on semantic matching: By introducing semantic weight w s and contextual relevance r c Dynamically adjust the integration priority of data fields. The formula is:

[0081]

[0082] Where S merge The matching score for merging two data tables is used to measure the priority of field matching in the two tables:

[0083] (F b ,G b ) is the set of fields to be integrated, F b , G b is the kth field in the field set to be merged, which comes from two different data tables; B is the total number of fields, that is, the number of fields that need to be matched in the data tables to be merged; Sim(F b ,G b ) indicates field F b and G b The semantic similarity of is calculated using a specific semantic matching algorithm (such as cosine similarity based on word vectors); w s is the semantic weight, which reflects the importance of the field in the monitoring task and is dynamically adjusted according to the impact of the field on the core indicators; r c Context dependency reflects the logical dependency between a field and its context fields.

[0084] Chaos optimization introduces dynamic threshold cleaning rules, combined with real-time error rate adjustment, to enhance the flexibility of large-scale data processing. Semantic matching and contextual relevance dynamically adjust field integration priorities, breaking through the traditional static integration approach based on format matching. These improvements enable the data processing module to improve data cleaning flexibility and format conversion accuracy when processing large-scale heterogeneous data, effectively reducing redundancy and consistency issues and providing high-quality input data for subsequent risk analysis.

[0085] (3) Dynamic monitoring module

[0086] It is used to conduct full-process dynamic monitoring of market supervision tasks based on the structured data. The full-process dynamic monitoring includes:

[0087] Monitoring of task execution: obtaining task name, start time, end time, execution status, duration, and execution log information;

[0088] Monitoring of data volume changes: Based on the data table change rate, identify data tables with significant data volume differences, calculate the data table change rate and compare it with the preset threshold to determine if the change rate|V new -V old | / V old If the value of V exceeds the preset threshold, it is considered to be significantly different and a data table warning message is generated. The data table warning message includes the name of the data table, the change rate, the abnormality type and the warning generation time. newIndicates the data volume of the current data table, that is, the number of records or storage capacity of the data table at the time of the most recent detection; V old Indicates the data volume of the data table during the previous detection, that is, the number of records or storage capacity of the data table during the previous monitoring;

[0089] By comparing V new and V old , can quantify the degree of change in the data table between two monitorings. If the change rate exceeds the preset threshold, the data table is considered to have changed significantly, thus triggering an early warning. For example: a data table has a record number of V in the last round of monitoring. old =10000, the number of records in this round of monitoring becomes V new =12000, the change rate is 12000-10000| / 10000×100%=20%. If the preset threshold of the change rate is 15%, the data table will be marked as "significant difference" and an early warning message will be generated;

[0090] Database resource monitoring: monitors database disk usage, including used space, remaining space, total space, and utilization, and generates resource utilization warning information;

[0091] Dynamic monitoring of core indicators: Real-time acquisition of changes in core indicators of market entities. The core indicators of market entities include the number of existing households, incremental trends, registered capital change rate, industry distribution trends and regional distribution trends.

[0092] (4) Risk Analysis Module

[0093] It is used to conduct intelligent analysis of abnormal behaviors or trends of market entities based on the real-time monitoring results of the dynamic monitoring module, extract features and classify risks of monitoring data through chaos immune optimization algorithm and migration immune algorithm, and generate risk warning reports including risk level, risk type and corresponding disposal suggestions.

[0094] In one specific embodiment, the risk analysis module includes:

[0095] The data preprocessing unit is used to obtain market entity monitoring data from the dynamic monitoring module and perform data cleaning and normalization;

[0096] Chaos immune optimization unit, used to extract features from pre-processed data using a chaos immune optimization algorithm, optimize the antibody population, and obtain a preliminary risk fitness value;

[0097] In the chaotic immune optimization unit, the dynamic weight function ω(t) is introduced, and the improved chaotic mapping formula is used to generate the antibody population. The formula is:

[0098] A(t+1)=μ·w(t)·A(t)·(1-A(t))+γ·f(A(t));

[0099] Wherein, t is the current iteration number, which indicates the algorithm optimization process. The more iterations, the smaller the mutation probability, and the optimization gradually converges. A(t) is the current risk characteristic value of the market subject, which is the comprehensive risk performance of the current subject at the monitoring moment obtained by combining the risk assessment model. A(t+1) is the risk characteristic value of the market subject after chaos optimization and perturbation at the next moment, which is used to predict the risk level at the future moment. μ is the chaos parameter, which controls the sensitivity of the market subject's risk characteristic changes and avoids local optimality by adjusting the optimization step size. γ is the perturbation coefficient, which is used to dynamically adjust the antibody generation accuracy to ensure that the risk details of the market subject can be more accurately reflected. f(A(t)) is the fitness function, which is calculated based on the risk deviation of the market subject and represents the difference between the actual risk level of the current subject and the expected benchmark value.

[0100] Calculate the fitness of each antibody: where R n is the risk factor value, is the benchmark value, n is the index of the current risk factor, indicating the nth specific risk indicator; N is the total number of risk factors, indicating the number of all risk indicators monitored. For example, if the risk factors monitored by the system include credit score, registered capital change rate, behavioral abnormality deviation, etc., and there are 5 factors in total, then N = 5;

[0101] In the process of population evolution, combined with the risk factor matrix R of the market entity f Adjust the mutation probability, the formula is:

[0102]

[0103] Where, P m is the mutation probability of the current market entity, which is used in the dynamic optimization algorithm to adjust the risk characteristics of the market entity and improve the search accuracy; m0 is the initial mutation probability, which indicates the initial disturbance intensity of the risk characteristics of market entities at the beginning of the algorithm; ||R f || is the Euclidean norm of the market entity risk factor, indicating the entity's comprehensive risk level;

[0104] The optimized antibody population A(t+1) is generated as the input of the migration immunity optimization unit.

[0105] Migration Immunity Optimization Unit, used to optimize migration and dynamically adapt risk factors across regions;

[0106] In the migration immunity optimization unit, market entities are divided according to industry or region to form distributed antibody nodes, and the migration probability is adjusted based on similarity and time decay:

[0107]

[0108] Where, P trans (i, j, t) is the probability of antibody migration of market entities from region i to region j, indicating the possibility that the optimization information of market entities in a certain region can be applied to other regions; Sim(A i ,A j ) is the similarity between the antibodies of market entities in region i and region j, which is used to measure the correlation between the risk characteristics of the two regions, for example, by calculating the cosine similarity through the risk indicators of the entities;

[0109] ∑ k≠i Sim(A i ,A k ) is the sum of the similarities between region i and all other regions, ensuring the normalization of the migration probability; λ is the time decay coefficient, which controls the decrease of the migration probability over time. A higher value indicates a rapid decrease in the influence of migration;

[0110] Recalculate the environmental fitness F for the migrated antibodies env :F env =a·R growth +β·R deviation , where R growth represents the growth rate, R deviation represents the risk deviation; α and β are the weight coefficients of growth rate and risk deviation respectively;

[0111] The optimized antibody population and fitness data are transmitted to the risk level assessment unit.

[0112] The risk level assessment unit is used to generate the risk level of the market entity based on the optimized risk data. The formula is:

[0113] f multi (x) = x1·f risk (x)+x2·f priority (x);

[0114] Where, f multi (x) is the final optimized value; f risk (x) is the risk deviation; f priority (x) is the priority of the regulatory task; x1 and x2 are the weight coefficients of risk assessment and regulatory task priority respectively; x represents the characteristic variable of the current market entity;

[0115] Risk levels are divided according to the final optimization value:

[0116] When the final optimized value f multi (x) When a market entity exceeds a set high-risk threshold, it is classified as a high-risk entity, which may have serious credit problems, abnormal growth or abnormal behavior, and requires priority supervision or emergency intervention measures;

[0117] When the final optimized value f multi (x) When a market entity is between the high-risk threshold and the low-risk threshold, it is classified as a medium-risk entity and may require regular monitoring and appropriate supervision;

[0118] When the final optimized value f multi (x) When the risk is less than the set low-risk threshold, the market entity is classified as a low-risk entity, which is usually characterized by stable operations or a good credit record and only requires routine supervision.

[0119] The risk type analysis unit is used to further analyze the risk types of market entities based on risk levels, including credit risk, abnormal behavior risk, and abnormal growth risk;

[0120] The risk warning report generation unit is used to generate a visual risk warning report based on the risk level and risk type.

[0121] (5) Visual display module

[0122] It is used to display the analysis results based on the risk analysis module, and to visualize the stock, growth, industry trends and regional distribution of market entities. The visualization methods include line charts, bar charts, geographic information system maps and report forms, which dynamically display risk warning information; and integrate early warning notification functions to send risk warning results to relevant regulatory departments via SMS, email or system push, supporting priority processing of risks at different levels and multi-department collaborative early warning mechanisms.

[0123] like Figure 2 FIG. 1 is another embodiment of the present invention, which provides an early warning method for a market supervision dynamic monitoring and intelligent risk early warning system based on big data, including the following steps:

[0124] S1: Collect market regulatory data from multiple heterogeneous data sources, cleanse, convert and integrate them, eliminate data redundancy and inconsistency, and generate unified structured data;

[0125] S2: Dynamically monitor the entire market supervision process based on structured data. Based on the real-time monitoring results of the dynamic monitoring module, conduct intelligent analysis of abnormal behaviors or trends of market entities. Use the chaotic immune optimization algorithm and the migration immune algorithm to extract features and classify risks from the monitoring data, generating a risk warning report that includes risk level, risk type, and corresponding disposal recommendations.

[0126] S3: Based on the analysis results of the risk analysis module, the stock, growth, industry trends and regional distribution of market entities are displayed in a visual way, and risk warning information is displayed dynamically; and the warning notification function is integrated to send the risk warning results to relevant regulatory departments via SMS, email or system push, supporting priority processing of risks at different levels and multi-department collaborative warning mechanism.

[0127] In summary, the present invention collects multidimensional data related to market supervision from multi-source heterogeneous data sources, realizes the unified integration of scattered data, solves the problem of data islands in traditional supervision systems, and significantly improves the comprehensiveness and availability of data; the data processing module cleans, converts and integrates the collected data, eliminates data redundancy and inconsistency, and generates unified structured data, laying a solid data foundation for subsequent dynamic monitoring and risk analysis, and improving data processing efficiency and accuracy; the dynamic monitoring module dynamically monitors the entire process of market supervision tasks based on structured data, covering task execution status, data volume changes, database resource monitoring, and real-time changes in core indicators of market entities (such as inventory, increments, industry distribution trends, etc.), effectively improving the ability to capture dynamic changes of market entities and enhancing real-time supervision. The risk analysis module combines the chaotic immune optimization algorithm and the migration immune algorithm to extract and classify the monitoring data of market entities, which can accurately identify the abnormal behavior or risk trend of market entities, and dynamically generate risk warning reports including risk level, risk type and corresponding disposal suggestions, providing regulatory authorities with scientific and intelligent risk analysis tools; the visualization display module dynamically displays the stock, increment, industry trend and regional distribution of market entities in the form of line charts, bar charts, geographic information system maps and reports, intuitively presenting risk distribution and changing trends, and also integrates early warning notification functions, which can send risk warning results to relevant regulatory authorities via SMS, email or system push, support priority processing of risks at different levels and multi-department collaborative early warning mechanism, and effectively optimize the allocation efficiency of regulatory resources. The present invention solves the problems of data dispersion, insufficient dynamic monitoring, delayed risk warning and poor visualization effect of the existing market supervision system through modular design and intelligent algorithms, realizes dynamic supervision and intelligent risk warning of the entire life cycle of market entities, and provides technical support for accurate decision-making of regulatory authorities.

[0128] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included within the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A market supervision dynamic monitoring and intelligent risk early warning system based on big data, characterized by: The system includes: a data collection module for collecting market supervision data from multiple heterogeneous data sources, including market entity registration information, business behavior data, credit records, law enforcement inspection data, consumer complaint data, food and drug supervision data, and external data related to market supervision; A data processing module is used to clean, convert and integrate the market supervision data, eliminate data redundancy and inconsistency, and generate unified structured data. The structured data includes basic attribute information of market entities, dynamic monitoring indicator data, and input data required for risk analysis; A dynamic monitoring module, used to dynamically monitor the entire process of market supervision tasks based on the structured data; The risk analysis module is used to conduct intelligent analysis of abnormal behaviors or trends of market entities based on the real-time monitoring results of the dynamic monitoring module. It uses the chaos immune optimization algorithm and the migration immune algorithm to extract features and classify risks from the monitoring data, and generates a risk warning report that includes risk level, risk type, and corresponding disposal suggestions; The visualization display module is used to visualize the stock, growth, industry trends, and regional distribution of market entities based on the analysis results of the risk analysis module. The visualization methods include line charts, bar charts, geographic information system maps, and reports, dynamically displaying risk warning information; and integrates early warning notification functions to send risk warning results to relevant regulatory departments via SMS, email, or system push, supporting priority processing of different levels of risks and a multi-department collaborative early warning mechanism; The data processing module adjusts the sensitivity of the data cleaning rule by generating a dynamic threshold by introducing a chaotic mapping function. The calculation formula of the dynamic threshold is: θ(t)=μ·θ(t-1)·(1-θ(t-1))+γ·f error (t); Where θ(t) is the dynamic threshold of the current iteration; θ(t-1) is the dynamic threshold of the previous iteration; μ is the chaos parameter; γ is the disturbance coefficient; f error (t) is the error function of the current iteration; By introducing the semantic weight w s and contextual relevance r c Dynamically adjust the integration priority of data fields. The formula is: Where S merge Score the match between the two tables: (F b ,G b ) is the set of fields to be integrated; B is the total number of fields; Sim(F b ,G b ) indicates field F b and G b The semantic similarity of w s is the semantic weight; r c is contextual relevance; The risk analysis module includes: The data preprocessing unit is used to obtain market entity monitoring data from the dynamic monitoring module and perform data cleaning and normalization; Chaos immune optimization unit, used to extract features from pre-processed data using a chaos immune optimization algorithm, optimize the antibody population, and obtain a preliminary risk fitness value; Migration Immunity Optimization Unit, used to optimize migration and dynamically adapt risk factors across regions; The risk level assessment unit is used to generate the risk level of the market entity based on the optimized risk data; The risk type analysis unit is used to further analyze the risk types of market entities based on risk levels, including credit risk, abnormal behavior risk, and abnormal growth risk; Risk warning report generation unit, used to generate visual risk warning reports based on risk level and risk type; In the chaotic immune optimization algorithm unit, a dynamic weight function ω(t) is introduced, and an improved chaotic mapping formula is used to generate the antibody population. The formula is: A(t+1)=μ·w(t)·A(t)·(1-A(t))+γ·f(A(t)); Where t is the current iteration number; A(t) is the current risk characteristic value of the market entity; A(t+1) is the risk characteristic value of the market entity after chaos optimization and perturbation at the next moment; μ is the chaos parameter; γ is the perturbation coefficient; f(A(t)) is the fitness function; calculate the fitness of each antibody: where R n is the risk factor value, is the benchmark value, n is the index of the current risk factor; N is the total number of risk factors; In the process of population evolution, combined with the risk factor matrix R of the market entity f Adjust the mutation probability, the formula is: Where, P m is the mutation probability of the current market entity; P m0 is the initial mutation probability; ||R f || is the Euclidean norm of the market entity risk factor; Generate the optimized antibody population A(t+1) as the input of the migration immunity optimization unit; In the migration immunity optimization unit, market entities are divided according to industry or region to form distributed antibody nodes, and the migration probability is adjusted based on similarity and time decay: Where, P trans (i, j, t) is the probability of antibody migration of market entities from region i to region j; Sim(A i ,A j ) is the similarity between the antibodies of the market players in region i and region j; ∑ k≠i Sim(A i ,A k ) is the sum of similarities between region i and all other regions; λ is the time decay coefficient; Recalculate the environmental fitness F for the migrated antibodies env :F env =α·R growth +β·R deviation , where R growth represents the growth rate, R deviation represents the risk deviation in the migration environment; α and β are the weight coefficients of growth rate and risk deviation respectively; The optimized antibody population and fitness data are transmitted to the risk level assessment unit; In the risk level assessment unit, the formula for generating the risk level of the market entity is: f multi (x)=ξ1·f risk (x)+ξ2·f priority (x); Where, f multi (x) is the final optimized value; f risk (x) is the risk deviation of the current market entity under the characteristic variable x; f priority (x) is the priority of the regulatory task; ξ1 and ξ2 are the weight coefficients of risk assessment and regulatory task priority respectively; x represents the characteristic variable of the current market entity; Risk levels are divided according to the final optimization value: When the final optimized value f multi (x) When a market entity exceeds a set high-risk threshold, it is classified as a high-risk entity and requires priority supervision or emergency intervention measures; When the final optimized value f multi (x) When a market entity is between the high-risk threshold and the low-risk threshold, it is classified as a medium-risk entity and requires regular monitoring and appropriate supervision; When the final optimized value f multi (x) When the risk is less than the set low-risk threshold, the market entity is classified as a low-risk entity and only requires routine supervision.

2. A market supervision dynamic monitoring and intelligent risk early warning system based on big data according to claim 1, characterized in that: The data collection module dynamically adjusts the collection priority based on the activity frequency and importance of the market entity. The activity frequency is calculated based on the real-time business behavior data of the market entity, and the importance is calculated based on the industry risk weight, credit score fluctuation and historical abnormal behavior frequency of the market entity. By defining the trust level and contribution level of data sources, the collection ratio of data sources is dynamically optimized. The formula is: Where, P v represents the priority acquisition probability of data source v; T v is the trustworthiness of data source v; C v is the contribution of data source v; V is the total number of data source types.

3. The market supervision dynamic monitoring and intelligent risk early warning system based on big data according to claim 1 is characterized in that: The full-process dynamic monitoring includes: Monitoring of task execution: obtaining task name, start time, end time, execution status, duration, and execution log information; Monitoring of data volume changes: Based on the data table change rate, identify data tables with significant data volume differences, calculate the data table change rate and compare it with the preset threshold to determine if the change rate|V new -V old | / V old If the value of V exceeds the preset threshold, it is considered to be significantly different and a data table warning message is generated. The data table warning message includes the name of the data table, the rate of change, the abnormality type and the warning generation time; wherein, V new Indicates the amount of data in the current data table, V old Indicates the data volume of the data table at the time of the previous detection; Database resource monitoring: monitors the database disk usage, including used space, remaining space, total space and utilization rate, and generates resource utilization warning information; Dynamic monitoring of core indicators: Real-time acquisition of changes in core indicators of market entities, including the number of existing households, incremental trends, registered capital change rate, industry distribution trends and regional distribution trends.

4. The early warning method of a market supervision dynamic monitoring and intelligent risk early warning system based on big data according to any one of claims 1 to 3, characterized in that: The method comprises: Collect market regulatory data from multiple heterogeneous data sources, cleanse, convert and integrate them, eliminate data redundancy and inconsistency, and generate unified structured data; Based on the structured data, the system dynamically monitors the entire process of market supervision tasks, and intelligently analyzes abnormal behaviors or trends of market entities based on the real-time monitoring results of the dynamic monitoring module. It extracts features and classifies risks from the monitoring data using the chaotic immune optimization algorithm and the migration immune algorithm, and generates a risk warning report that includes risk level, risk type, and corresponding disposal recommendations. Based on the analysis results of the risk analysis module, the stock, growth, industry trends and regional distribution of market entities are displayed in a visual way, and risk warning information is displayed dynamically; and the warning notification function is integrated to send risk warning results to relevant regulatory departments via SMS, email or system push, supporting priority processing of risks at different levels and multi-department collaborative warning mechanism.

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